High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing.

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Strongly Confined CsPbBr3 Quantum Dots as Quantum Emitters and Building Blocks for Rhombic Superlattices.

The success of the colloidal semiconductor quantum dots (QDs) field is rooted in the precise synthetic control of QD size, shape, and composition, enabling electronically well-defined functional nanomaterials that foster fundamental science and motivate diverse fields of applications. While the exploitation of the strong confinement regime has been driving commercial and scientific interest in InP or CdSe QDs, such a regime has still not been thoroughly explored and exploited for lead-halide perovskite QDs, mainly due to a so far insufficient chemical stability and size monodispersity of perovskite QDs smaller than about 7 nm. Here, we demonstrate chemically stable strongly confined 5 nm CsPbBr3 colloidal QDs via a postsynthetic treatment employing didodecyldimethylammonium bromide ligands. The achieved high size monodispersity (7.5% ± 2.0%) and shape-uniformity enables the self-assembly of QD superlattices with exceptional long-range order, uniform thickness, an unusual rhombic packing with an obtuse angle of 104°, and narrow-band cyan emission. The enhanced chemical stability indicates the promise of strongly confined perovskite QDs for solution-processed single-photon sources, with single QDs showcasing a high single-photon purity of 73% and minimal blinking (78% "on" fraction), both at room temperature.

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity.

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

Assessing the Drawbacks and Benefits of Ion Migration in Lead Halide Perovskites.

Since the inception of the unprecedented rise of halide perovskites for photovoltaic research, ion migration has shadowed this material class with undesirable hysteresis and degradation effects, limiting its practical implementations. Unfortunately, the localized doping and electrochemical reactions triggered by ion migration cause many more undesirable effects that are often unreported or misinterpreted because they deviate from classical semiconductor behavior. In this Perspective, we provide a concise overview of such effects in halide perovskites, such as operational instability in photovoltaics, polarization-induced abnormal external quantum efficiency in light-emitting diodes, and energy channel shift and anomalous sensitivities in hard radiation detection. Finally, we highlight a unique use case of exploiting ion migration as a boon to design emerging memory technologies such as memristors for information storage and computing.

Structural Diversity in Multicomponent Nanocrystal Superlattices Comprising Lead Halide Perovskite Nanocubes.

Nanocrystal (NC) self-assembly is a versatile platform for materials engineering at the mesoscale. The NC shape anisotropy leads to structures not observed with spherical NCs. This work presents a broad structural diversity in multicomponent, long-range ordered superlattices (SLs) comprising highly luminescent cubic CsPbBr3 NCs (and FAPbBr3 NCs) coassembled with the spherical, truncated cuboid, and disk-shaped NC building blocks. CsPbBr3 nanocubes combined with Fe3O4 or NaGdF4 spheres and truncated cuboid PbS NCs form binary SLs of six structure types with high packing density; namely, AB2, quasi-ternary ABO3, and ABO6 types as well as previously known NaCl, AlB2, and CuAu types. In these structures, nanocubes preserve orientational coherence. Combining nanocubes with large and thick NaGdF4 nanodisks results in the orthorhombic SL resembling CaC2 structure with pairs of CsPbBr3 NCs on one lattice site. Also, we implement two substrate-free methods of SL formation. Oil-in-oil templated assembly results in the formation of binary supraparticles. Self-assembly at the liquid-air interface from the drying solution cast over the glyceryl triacetate as subphase yields extended thin films of SLs. Collective electronic states arise at low temperatures from the dense, periodic packing of NCs, observed as sharp red-shifted bands at 6 K in the photoluminescence and absorption spectra and persisting up to 200 K.

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing.

Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. "Reconfigurable memristors" that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes - volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices.

An adaptive device for AI neural networks.

[Figure: see text].

Shape-Directed Co-Assembly of Lead Halide Perovskite Nanocubes with Dielectric Nanodisks into Binary Nanocrystal Superlattices.

Self-assembly of colloidal nanocrystals (NCs) holds great promise in the multiscale engineering of solid-state materials, whereby atomically engineered NC building blocks are arranged into long-range ordered structures-superlattices (SLs)-with synergistic physical and chemical properties. Thus far, the reports have by far focused on single-component and binary systems of spherical NCs, yielding SLs isostructural with the known atomic lattices. Far greater structural space, beyond the realm of known lattices, is anticipated from combining NCs of various shapes. Here, we report on the co-assembly of steric-stabilized CsPbBr3 nanocubes (5.3 nm) with disk-shaped LaF3 NCs (9.2-28.4 nm in diameter, 1.6 nm in thickness) into binary SLs, yielding six columnar structures with AB, AB2, AB4, and AB6 stoichiometry, not observed before and in our reference experiments with NC systems comprising spheres and disks. This striking effect of the cubic shape is rationalized herein using packing-density calculations. Furthermore, in the systems with comparable dimensions of nanocubes (8.6 nm) and nanodisks (6.5 nm, 9.0 nm, 12.5 nm), other, noncolumnar structures are observed, such as ReO3-type SL, featuring intimate intermixing and face-to-face alignment of disks and cubes, face-centered cubic or simple cubic sublattice of nanocubes, and two or three disks per one lattice site. Lamellar and ReO3-type SLs, employing large 8.6 nm CsPbBr3 NCs, exhibit characteristic features of the collective ultrafast light emission-superfluorescence-originating from the coherent coupling of emission dipoles in the excited state.

Halide perovskite memristors as flexible and reconfigurable physical unclonable functions.

Physical Unclonable Functions (PUFs) address the inherent limitations of conventional hardware security solutions in edge-computing devices. Despite impressive demonstrations with silicon circuits and crossbars of oxide memristors, realizing efficient roots of trust for resource-constrained hardware remains a significant challenge. Hybrid organic electronic materials with a rich reservoir of exotic switching physics offer an attractive, inexpensive alternative to design efficient cryptographic hardware, but have not been investigated till date. Here, we report a breakthrough security primitive exploiting the switching physics of one dimensional halide perovskite memristors as excellent sources of entropy for secure key generation and device authentication. Measurements of a prototypical 1 kb propyl pyridinium lead iodide (PrPyr[PbI3]) weak memristor PUF with a differential write-back strategy reveals near ideal uniformity, uniqueness and reliability without additional area and power overheads. Cycle-to-cycle write variability enables reconfigurability, while in-memory computing empowers a strong recurrent PUF construction to thwart machine learning attacks.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks.

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.

Cubic NaSbS2 as an Ionic-Electronic Coupled Semiconductor for Switchable Photovoltaic and Neuromorphic Device Applications.

The recent emergence of lead halide perovskites as ionic-electronic coupled semiconductors motivates the investigation of alternative solution-processable materials with similar modulatable ionic and electronic transport properties. Here, a novel semiconductor-cubic NaSbS2 -for ionic-electronic coupled transport is investigated through a combined theoretical and experimental approach. The material exhibits mixed ionic-electronic conductivity in inert atmosphere and superionic conductivity in humid air. It is shown that post deposition electronic reconfigurability in this material enabled by an electric field induces ionic segregation enabling a switchable photovoltaic effect. Utilizing post-perturbation of the ionic composition of the material via electrical biasing and persistent photoconductivity, multistate memristive synapses with higher-order weight modulations are realized for neuromorphic computing, opening up novel applications with such ionic-electronic coupled materials.

Perturbation-Induced Seeding and Crystallization of Hybrid Perovskites over Surface-Modified Substrates for Optoelectronic Devices.

Growing a monocrystalline layer of lead halide perovskites directly over substrates is necessary to completely harness their stellar properties in optoelectronic devices, as the single crystals of these materials are extremely brittle. We study the crystallization mechanism of perovskites by antisolvent vapor diffusion to its precursor solution and find that heterogeneous nucleation prevails in the process, with the crystallization dish walls providing the energy to overcome the nucleation barrier. By perturbing the system using sonication, we are able to introduce homogeneously nucleated seed crystals in the precursor solution. These seeds lead to the growth of closely packed crystals over surface-modified substrates kept in the precursor solution. This crystallization process is substrate independent and scalable and can be utilized to fabricate planar optoelectronic devices. We demonstrate a methylammonium lead iodide planar crystal photoconductor with a colossal detectivity of 1.48 × 1013 Jones.

Completely Solvent-free Protocols to Access Phase-Pure, Metastable Metal Halide Perovskites and Functional Photodetectors from the Precursor Salts.

Mechanochemistry is a green, solid-state, re-emerging synthetic technique that can rapidly form complex molecules and materials without exogenous heat or solvent(s). Herein, we report the application of solvent-free mechanochemical ball milling for the synthesis of metal halide perovskites, to overcome problems with solution-based syntheses. We prepared phase-pure, air-sensitive CsSnX3 (X = I, Br, Cl) and its mixed halide perovskites by mechanochemistry for the first time by reactions between cesium and tin(II) halides. Notably, we report the sole examples where metastable, high-temperature phases like cubic CsSnCl3, cubic CsPbI3, and trigonal FAPbI3 were accessible at ambient temperatures and pressures without post-synthetic processing. The perovskites can be prepared up to "kilogram scales." Lead-free, all-inorganic photodetector devices were fabricated using the mechanosynthesized CsSnBr1.5Cl1.5 under solvent-free conditions and showed 10-fold differences between on-off currents. We highlight an essentially solvent-free, general approach to synthesize metastable compounds and fabricate photodetectors from commercially available precursors.

Field-Driven Athermal Activation of Amorphous Metal Oxide Semiconductors for Flexible Programmable Logic Circuits and Neuromorphic Electronics.

Despite extensive research, large-scale realization of metal-oxide electronics is still impeded by high-temperature fabrication, incompatible with flexible substrates. Ideally, an athermal treatment modifying the electronic structure of amorphous metal oxide semiconductors (AMOS) to generate sufficient carrier concentration would help mitigate such high-temperature requirements, enabling realization of high-performance electronics on flexible substrates. Here, a novel field-driven athermal activation of AMOS channels is demonstrated via an electrolyte-gating approach. Facilitating migration of charged oxygen species across the semiconductor-dielectric interface, this approach modulates the local electronic structure of the channel, generating sufficient carriers for charge transport and activating oxygen-compensated thin films. The thin-film transistors (TFTs) investigated here depict an enhancement of linear mobility from 51 to 105.25 cm2 V-1 s-1 (ionic-gated) and from 8.09 to 14.49 cm2 V-1 s-1 (back-gated), by creating additional oxygen vacancies. The accompanying stochiometric transformations, monitored via spectroscopic measurements (X-ray photoelectron spectroscopy) corroborate the detailed electrical (TFT, current evolution) parameter analyses, providing critical insights into the underlying oxygen-vacancy generation mechanism and clearly demonstrating field-induced activation as a promising alternative to conventional high-temperature annealing strategies. Facilitating on-demand active programing of the operation modes of transistors (enhancement vs depletion), this technique paves way for facile fabrication of logic circuits and neuromorphic transistors for bioinspired computing.

Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations.

Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of accessible memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.

Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation.

Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short- and long-term plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material.

Indium Tungsten Oxide Thin Films for Flexible High-Performance Transistors and Neuromorphic Electronics.

Thin-film transistors (TFTs) with high electrical performances (mobility > 10 cm2/V s, Vth < 1 V, SS < 1 V/decade, on/off ratio ≈ 106) obtained from the silicon- and oxide-based single-crystalline semiconductor materials require high processing temperature and hence are not suitable for flexible electronics. Amorphous oxide-based transparent electronic devices are attractive to meet emerging technological demands where crystalline oxide-/silicon-based architectures cannot provide a solution. Here, we tackle this problem by using a novel amorphous oxide semiconducting material-namely, indium tungsten oxide (IWO)-as the active channel in flexible TFTs (FTFTs). Post-annealing temperature as low as 270 °C for amorphous IWO thin films deposited by radio frequency sputtering at room temperature could result in smooth morphology ( Rrms ≈ 0.42 nm), good adhesion, and high carrier density ( n ≈ 7.19 × 1018 cm-3). Excellent TFT characteristics of flexible devices could be achieved with linear field effect mobility µFE ≈ 25.86 cm2/V s, subthreshold swing SS ≈ 0.30 V/decade, threshold voltage Vth ≈ -1.5 V, and on/off ratio Ion/ Ioff ≈ 5.6 × 105 at 3 V and stable operation during bending of the FTFT. Additionally, IWO TFTs were implemented as synapses, the building block for neuromorphic computing. Paired-pulse facilitation up to 138% was observed and showed an exponential decay resembling chemical synapses. Utilizing this characteristic, a high-pass dynamic temporal filter was devised providing increased gain from 1.55 to 21 when frequency was raised from 22 to 62 Hz. The high performance and stability of flexible TFTs obtained with IWO films demonstrate their promise for low-voltage electronic applications.

Doping and Switchable Photovoltaic Effect in Lead-Free Perovskites Enabled by Metal Cation Transmutation.

Creating defect tolerant lead-free halide perovskites is the major challenge for development of high-performance photovoltaics with nontoxic absorbers. Few compounds of Sn, Sb, or Bi possess ns2 electronic configuration similar to lead, but their poor photovoltaic performances inspire us to evaluate other factors influencing defect tolerance properties. The effect of heavy metal cation (Bi) transmutation and ionic migration on the defects and carrier properties in a 2D layered perovskite (NH4 )3 (Sb(1-x) Bix )2 I9 system is investigated. It is shown, for the first time, the possibility of engineering the carriers in halide perovskites via metal cation transmutation to successfully form intrinsic p- and n-type materials. It is also shown that this material possesses a direct-indirect bandgap enabling high absorption coefficient, extended carrier lifetimes >100 ns, and low trap densities similar to lead halide perovskites. This study also demonstrates the possibility of electrical poling to induce switchable photovoltaic effect without additional electron and hole transport layers.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS2 ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.